A novel Transformer-based model with large kernel temporal convolution for chemical process fault detection

Author:

Zhu Zhichao,Chen Feiyang,Ni Lei,Bian Haitao,Jiang Juncheng,Chen Zhiquan

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference52 articles.

1. A data-driven Bayesian network learning method for process fault diagnosis;Amin;Process Saf. Environ. Prot.,2021

2. An analysis of process fault diagnosis methods from safety perspectives;Arunthavanathan;Comput. Chem. Eng.,2021

3. Bai, S., Kolter, J.Z., Koltun, V., 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.

4. A novel transformer-based multi-variable multi-step prediction method for chemical process fault prognosis;Bai;Process Saf. Environ. Prot.,2023

5. One step forward for smart chemical process fault detection and diagnosis;Bi;Comput. Chem. Eng.,2022

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